Misconceptions about being a data scientist

21 Oct 2022

Every stream or job profile, when put under immense and sudden growth, becomes the talk of the town. And when this happens, it’s natural to expect some confusion with the meaning, career idea, and responsibilities that the particular profile. Therefore, with myths coming on the rise, let’s break down misconceptions related to data scientists.

Data is as essential as knowledge in this growing tech-based era. Everything we do, every word we speak on the internet and even the processes that run on our phones, and other devices run on data. With data booming all over us, there is a need for a particular category or alignment for this data; and we definitely need someone to take care of this process.

This is where the role of data scientists comes to play. As a growing stream, data scientists have a lot of myths about what they do and what they deal with. In this article, let’s break down some of the most common myths or misconceptions related data scientists.

Myth #1: Data scientists are not statisticians

First of all, a lot of data scientists are actually from managerial or non-mathematical backgrounds. This does not mean that no data scientist is involved with numbers, a statistics degree prove to be an upper hand in this industry.

However, you can start playing with data and machine learning models with little knowledge of statistics.

Data science is a large field. It is composed of several other disciplines where a knowledge of statistics is required ––– basic knowledge or advanced. For instance, if you are working on developing a data processing pipeline, you wouldn’t need an advanced knowledge of statistics. However, if you plan on advancing in the stream of machine learning and want to work on deep models then statistical knowledge is needed.

Myth #2: Hardcore programming? Not really

It’s almost a false assumption that data scientists need to know programming languages like the alphabet. It’s an assumption that data scientists need to know how to code and should be aware of algorithms. However, programming is essential because a programmer can easily and readily customize the code. When a programmer writes a program, it is easy to generate an analysis.

Data science, as a new industry, focuses on multiple arrays of jobs. While coding can be considered a part of data science, it is not the only part.

A data scientist will also need to meet with the stakeholders to understand the issue that they would like solved. The data scientist would also need to present these results to them and see the impact of their work.

Myth #3: Data science is not really science

Data science is considered the mother of all sciences. As it comprises the scientific method and is the precursor to any science. The foundation of science—physics, chemistry, biology, social sciences, psychology, etc., is founded on hypothesis, by experimenting and analyzing those observations that are obtained and interpreting the observations from those experiments.

For all this, there will be certain techniques and tools that one should use. These techniques require skills that are integral in data science.

Myth #4: Data scientists write their own models

Data scientists are not entirely programmers. As explained above, a variety of skills encompass being a data scientist, and coding is not an integral part. There are programmers who write the libraries and models that data scientists use. It is not all about coding.

Data scientists can automate the algorithms needed and choose the one that coincides with the mathematics of the specified dataset and the type of output that is required, pushing out the code part of it entirely. However, a deep understanding of mathematics is required.

When it comes to data science, it cannot be put under one umbrella that can explain everything in sequence. It’s a growing field and with this comes room for confusion. However, the more we try and equip ourselves with the right information, misconceptions can be pushed out entirely.

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